code: def data(self):
      import logging
  
      logger = logging.getLogger("PyGNativeTrainer")
      logger.info("getting data")
  
      if self.__data is None:
          self.__data = HeteroData()
  
          for node_type, x in self.__dataset.x_dict.items():
              logger.debug(f"getting x for {node_type}")
              self.__data[node_type].x = x
              self.__data[node_type][
                  "num_nodes"
              ] = self.__dataset.num_nodes(node_type)
  
          for node_type, y in self.__dataset.y_dict.items():
              logger.debug(f"getting y for {node_type}")
              self.__data[node_type]["y"] = y
  
          for node_type, train in self.__dataset.train_dict.items():
              logger.debug(f"getting train for {node_type}")
              self.__data[node_type]["train"] = train
  
          for node_type, test in self.__dataset.test_dict.items():
              logger.debug(f"getting test for {node_type}")
              self.__data[node_type]["test"] = test
  
          for node_type, val in self.__dataset.val_dict.items():
              logger.debug(f"getting val for {node_type}")
              self.__data[node_type]["val"] = val
  
          for (
              can_edge_type,
              ei,
          ) in self.__dataset.edge_index_dict.items():
              logger.info("converting to csc...")
              ei["dst"] = index2ptr(
                  ei["dst"], self.__dataset.num_nodes(can_edge_type[2])
              )
  
              logger.info("updating data structure...")
              self.__data.put_edge_index(
                  layout="csc",
                  edge_index=list(ei.values()),
                  edge_type=can_edge_type,
                  size=(
                      self.__dataset.num_nodes(can_edge_type[0]),
                      self.__dataset.num_nodes(can_edge_type[2]),
                  ),
                  is_sorted=True,
              )
              gc.collect()
  
      return self.__data
  
imports: from trainers import Trainer
  from trainers import extend_tensor
  from datasets import OGBNPapers100MDataset
  from models.pyg import GraphSAGE
  import torch
  import numpy
  import torch.distributed
  from torch.nn.parallel import DistributedDataParallel
  import torch.nn.functional
  from torch_geometric.utils.sparse import index2ptr
  from torch_geometric.data import HeteroData
  from torch_geometric.loader import NeighborLoader
  import gc
  import os
  import time
label_desc: Use image node_id 5 for calling the PyGNativeTrainer obj's underlying member method code with example usage: obj.data() and returns: self